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teaching:ws2019:intellisys_ws2019 [2019/07/05 15:12] Qadeer Khan |
teaching:ws2019:intellisys_ws2019 [2019/07/05 15:20] Qadeer Khan |
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| **Organizers**: | **Organizers**: | ||
| [[members: | [[members: | ||
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| + | Assignment to the lab will be done via the matching system (http:// | ||
| The preliminary meeting (recommended) to take place on **Wednesday, | The preliminary meeting (recommended) to take place on **Wednesday, | ||
| - | Slides pertaining to the lab will be made available after the preliminary meeting | + | Slides pertaining to the lab will be made available after the preliminary meeting. |
| + | |||
| + | == Course Content == | ||
| + | Learning-based approaches have recently made tremendous progress in the | ||
| + | computer vision and robotics research community. In this practical | ||
| + | course, we will solve challenging real-world problems in the area of | ||
| + | self-driving cars and intelligent systems. Topics will be in the | ||
| + | direction of perception, control, and environmental understanding. This | ||
| + | may also involve working on massive datasets including unstructured or | ||
| + | unordered data.\\ | ||
| + | |||
| + | == Pre-requisites == | ||
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| + | * Good knowledge of the Python language and basic mathematics such as linear algebra, analysis, and numerics is required. | ||
| + | * Good knowledge of a deep learning framework such as PyTorch, TensorFlow, etc. | ||
| + | * Participation in at least one of the offered deep learning lectures at TUM is required. | ||
